Feature extraction from ECG signals using wavelet transforms for disease diagnostics

Computerized Interpretation of Cardiovascular Physiological Signals

“…Take computerized ECG arrhythmia interpretation for an example. It has been confirmed that Hermite basis functions (HBFs) and wavelet energy descriptors are among those most competitive ones for feature characterization in discrimination analysis (Senhadii et al, 1995;Rasiah et al, 1997;AI-Farhoum & Howitt 1999;Lagerholm et al, 2000;Saxena et al, 2002;Linh et al, 2003;Engin 2007).…”

“…Actually, signal representation in time domain is legible but redundant, which may be evidenced by means of PCA (Geva 1998;Stamkopoulos et al, 1998). As a consequence, morphological analysis was generally combined with domain transformation, such as Hilbert transform (Bolton & Westphal 1981), HD (Rasiah et al, 1997;Lagerholm et al, 2000;Linh et al, 2003) and WT (Senhadii et al, 1995;AI-Farhoum & Howitt 1999;Saxena et al, 2002;Engin 2007), in those published paradigms of computerized physiological signal interpretation. Domain transformation, unlike direct morphological analysis, attempts to characterize physiological signals in an alternative space, where the genuine signal components are more discernible from noises and artefacts.…”

Application of Computational Intelligence Techniques for Cardiovascular Diagnostics

“…This transformation of the ECG signals has been carried out in the past using techniques such as autocorrelation function, time frequency analysis, and wavelet transforms (WT) (Maglaveras, Stamkopoulos et al 1998;Addison, Watson et al 2000;Kundu, Nasipuri et al 2000;Dokur and Olmez 2001;Saxena, Kumar et al 2002). Results of these and other studies in the literature have demonstrated that WT is the most promising method to extract features that characterize the behavior of ECG signals in an effective manner.…”

Information Extraction from Multimodal ECG Documents

“…These spectrums can be generated using either Wavelet transformation (Saxena et al [7] and Ghaffari et al [3]) or Fourier transformation (Tompkins [9]). The peaks of the frequency spectrum obtained corresponds to the peak energy of the QRS complex.…”